1. Point Control Mode (PTP)
Point position control is widely used in the field of mechatronics and robotics industry. Typical applications of CNC machine tool tracking part contour, industrial robot fingertip trajectory control and walking robot path tracking system in machinery manufacturing.
In the control process, the industrial robot is required to be able to move quickly and accurately between adjacent points, and there is no regulation for the movement trajectory to reach the target point.
Positioning accuracy and time required to move are the two main technical indicators of this control mode. This control method is easy to achieve with low positioning accuracy and is typically used for loading, unloading, and handling spot welds, where the inserted part on the board should maintain the exact position of the end effector at the target point. This method is relatively simple, but it is difficult to achieve the positioning accuracy of 2-3um.
The point control system is actually a position servo system. Its basic structure and composition are basically the same, but the control complexity varies with different emphases. According to the feedback, it can be divided into closed-loop system, semi-closed-loop system and open-loop system.
2. Continuous Trajectory Control Mode (CP)
Under the control of the point position, the start and end speed of the PTP is 0, during which various speed planning methods can be used.
CP control is the continuous control of the position of the industrial robot end effector in the workspace. The velocity at the midpoint is not zero. It's constantly moving. The velocity of each point is obtained by looking forward at the velocity. Generally speaking, continuous trajectory control mainly adopts the speed forward-looking method: forward speed limit, corner speed limit, tracking speed limit, maximum speed limit, and profile error speed limit.
The joints of industrial robots are continuous and continuous, and with synchronous movements, the end effector can form a continuous trajectory. The main technical indicators of this control mode are the tracking accuracy and stability of the position of the end actuator of the industrial robot, usually arc welding and painting. This control method is used for robotic dedeburring and inspection.
3. Force (torque) control method
With the continuous expansion of the application boundaries of robots, visual empowerment alone can no longer meet the needs of complex practical applications. In this case, a force/moment must be introduced to control the output, or a force or moment must be introduced as a closed-loop feedback.
When the object is grabbed and placed, the assembly is in progress, and in addition to precise positioning, the appropriate force or torque needs to be used, and then a (torque) servo must be used. The control principle is basically the same as that of position servo control, but the input and feedback are not position signals, but force (torque) signals, so powerful (torque) sensors must be used in the system. Sensing functions such as proximity, adaptive control, and swipe are also sometimes used.
4. Intelligent control mode
Robot intelligent control is a control mode that controls intelligent information processing, intelligent information feedback and intelligent control decision-making through sensors (such as cameras). Image sensors, ultrasonic emitters, lasers, conductive rubbers, piezoelectric and pneumatic elements, travel switches and other electromechanical components) to gain knowledge of their surroundings and make decisions based on their own internal knowledge base.
The development of intelligent control technology relies on the rapid development of artificial intelligence expert systems such as artificial neural networks, genetic algorithms, and genetic algorithms. In recent years, significant progress has been made in intelligent control technology. Fuzzy control theory, artificial neural network theory and their integration have greatly improved the speed and accuracy of robots. It is mainly used for multi-joint robot tracking control, lunar robot control, weeding robot control, cooking robot control, etc.
Robot intelligent control can be divided into: fuzzy control, adaptive control, optimal control, neural network control, fuzzy neural network control and expert control.
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